1-D Sampling. Letf s = 1/T be the sampling frequency. What is the relationship between S(e jω ) and G(f)? S(e jω ) = 1. Intuition Scale frequencies - PDF

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C. A. Bouman: Digital Image Processing - January 9, D Sampling g(t) Continuous timeinput Sampler PeriodT s(n) = g(nt) Discrete timeoutput Letf s = 1/T be the sampling frequency. What is the relationship

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C. A. Bouman: Digital Image Processing - January 9, D Sampling g(t) Continuous timeinput Sampler PeriodT s(n) = g(nt) Discrete timeoutput Letf s = 1/T be the sampling frequency. What is the relationship between S(e jω ) and G(f)? S(e jω ) = 1 ( ) ω 2πk G T 2πT Intuition Scale frequencies Replicate at period 2π Apply gain factor of 1 T. k= f = 0 ω = 0 f = 1 2T = 1 2 f s ω = π f = 1 T = f s ω = 2π .. C. A. Bouman: Digital Image Processing - January 9, D Sampling 1 G(f) 1 2 f s f s 1 T S(e jω ) 4π 3π 2π π 0 π 2π 3π 4π Scale frequencies Replicate at period 2π Apply gain factor of 1 T. CT Frequency DT Frequency f s π f s 2π C. A. Bouman: Digital Image Processing - January 9, D Sampling g(x,y) Continuous space input Sampler period ( T, T ) x y s(m,n) = g(mt x,nt y) Discrete space output Let T x and T y be the sampling period in the x and y dimensions. Then S(e jµ,e jν ) = 1 T x T y Intuition Scale frequencies k= l= G ( µ 2πk, ν 2πl ) 2πT x 2πT y (u,v) = (0,0) (µ,ν) = (0,0) (u,v) = ( 1 2T x,0) (µ,ν) = (π,0) (u,v) = (0, (u,v) = ( 1 2T x, 1 2T y ) (µ,ν) = (0,π) 1 2T y ) (µ,ν) = (π,π) C. A. Bouman: Digital Image Processing - January 9, Replicate along both µ and ν with period 2π Apply gain factor of 1 T x T y. C. A. Bouman: Digital Image Processing - January 9, Example 1: 2-D Sampling Without Aliasing G(u,v) - Spectrum of continuous space image. v 1/(2 T y ) 1/(2T ) x u S(e jµ,e jν ) - Spectrum of sampled image. v u C. A. Bouman: Digital Image Processing - January 9, Example 2: 2-D Sampling With Aliasing G(u,v) - Spectrum of continuous space image. v 1/(2 T y ) 1/(2T ) x u S(e jµ,e jν ) - Spectrum of sampled image. C. A. Bouman: Digital Image Processing - January 9, Nyquist Condition A continuous-space signal, g(x, y), may be uniquely reconstructed from its sampled version,s(m,n), ifg(u,v) = 0 for all u 1 2T x and v 1 2T y. This condition is sufficient, but not necessary. C. A. Bouman: Digital Image Processing - January 9, Example 3: Nonrectangular Spectral Support G(u,v) - Spectrum of continuous space image. v 1/(2 T y ) 1/(2T ) x u S(e jµ,e jν ) - Spectrum of sampled image. C. A. Bouman: Digital Image Processing - January 9, Focal Plain Arrays Typical Charged Coupled Devices (CCD) Imaging array A CCD Cell T T Solid state device used in video and still cameras. Each cell collects photons in a squaret T region. Response of each cell is linear with energy (photons). Signal is shifted out after capture. Cell should be large for best sensitivity. Finite cell size violates sampling assumptions. C. A. Bouman: Digital Image Processing - January 9, Mathematical Model for CCD Lets(m,n) be the output of cell (m,n), then s(m,n) = h(x mt,y nt)g(x,y)dxdy IR 2 whereh(x,y) is the rectangular window for each cell. h(x,y) = 1 T 2rect(x/T,y/T) Define g(x,y) so that g(ξ,η) = h(x ξ,y η)g(x,y)dxdy IR 2 = h( x, y) g(x,y) and then we have that s(m,n) = g(mt,nt) C. A. Bouman: Digital Image Processing - January 9, CCD Model in Space Domain Filter signal with space reversed cell profile g(x,y) = h( x, y) g(x,y) Sample filtered image = 1 T 2rect(x/T,y/T) g(x,y) s(m,n) = g(mt,nt) Cell aperture blurs image. C. A. Bouman: Digital Image Processing - January 9, CCD Model in Frequency Domain Filter signal with cell profile G(u,v) = H (u,v)g(u,v) = sinc(ut, vt)g(u, v) Sample filtered image S(e jµ,e jν ) = 1 T 2 k= l= ( ) µ 2πk 2πl G,ν 2πT 2πT Complete model S(e jµ,e jν ) = 1 T 2 k= l= Sinc function filters image. ( µ 2πk sinc 2π, ν 2πl ) 2π ( ) µ 2πk 2πl G,ν 2πT 2πT C. A. Bouman: Digital Image Processing - January 9, Sampled Image Display or Rendering (Reconstruction) CRT s and LCD displays convert discrete-space images to continuous-space images. Notation: Model: s(m,n) - sampled image p(x,y) - point spread function (PSF) of display f(x, y) - displayed image In space domain: f(x,y) = m= n= In frequency domain: s(m,n)p(x mt,y nt) F(u,v) = P(u,v)S(e j2πtu,e j2πtv ) µ 2πTu ν 2πTv Monitor PSF further softens image. C. A. Bouman: Digital Image Processing - January 9, Model for Sampling and Reconstruction Combining models for sampling and reconstruction results in: F(u,v) = P(u,v) T 2 k= l= When no aliasing occurs, this reduces to ( H u K T,v l ) ( G u K T T,v l ) T F(u,v) = P(u,v)H (u,v) G(u,v) T 2 = P(u,v) sinc(ut,vt) G(u,v) T 2 C. A. Bouman: Digital Image Processing - January 9, Effect of Sampling and Reconstruction The image is effectively filtered by the transfer function 1 T 2P(u,v)H (u,v) = 1 T 2P(u,v)sinc(uT,vT) Scanned image normally must be sharpened to remove the effect of softening produced in the scanning and display processes. C. A. Bouman: Digital Image Processing - January 9, Raster Scan Ordering Specific scan pattern for mapping 2-D images to 1-D. Order pixels from top to bottom and left to right. Example: Consider the discrete-space image f(m,n) f(0,0) f(m 1,0)..... f(0,n 1) f(m 1,N 1) Raster ordering produces a 1-D signalx n x 0 x 1 x M 1 x M x M+1 x 2 M x (N 1) M x (N 1) M+1 x N M 1 C. A. Bouman: Digital Image Processing - January 9, Vector Representation of Images An image is not a matrix. (A Matrix specifies a linear function.) Vectorizing images Often image must be converted to a vector (data). Vector looks like x = f(0,0). f(m 1,0). f(0,n 1). f(m 1,N 1) Mapping from vector to image isf(m,n) = x n M+m.
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